mTRF-Toolbox

버전 2.7 (37.7 MB) 작성자: Mick Crosse
A MATLAB package for modelling multivariate stimulus-response data
5.0
(5)
다운로드 수: 736
업데이트 날짜: 2024/12/1
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인용 양식

Crosse, Michael J., et al. “Linear Modeling of Neurophysiological Responses to Speech and Other Continuous Stimuli: Methodological Considerations for Applied Research.” Frontiers in Neuroscience, vol. 15, Frontiers Media SA, Nov. 2021, doi:10.3389/fnins.2021.705621.

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examples

examples/libs/cnsp_utils

examples/libs/cnsp_utils/cnd

examples/libs/cnsp_utils/dili_aud2sgram

examples/libs/filedownload

mtrf

examples

GitHub 디폴트 브랜치를 사용하는 버전은 다운로드할 수 없음

버전 게시됨 릴리스 정보
2.7

See release notes for this release on GitHub: https://github.com/mickcrosse/mTRF-Toolbox/releases/tag/v2.7
2.6

See release notes for this release on GitHub: https://github.com/mickcrosse/mTRF-Toolbox/releases/tag/v2.6
2.5

See release notes for this release on GitHub: https://github.com/mickcrosse/mTRF-Toolbox/releases/tag/v2.5
2.4

See release notes for this release on GitHub: https://github.com/mickcrosse/mTRF-Toolbox/releases/tag/v2.4
2.3

The following updates were made to v2.3:

1. Fixed correlation broadcasting issue for older versions
2. Fixed verbose issue for older versions

Thanks to Maya Kaufman for flagging the above issues.
2.2

The following updates were made to v2.2:

1. Fixed MSE input argument bug
2. Stabilized correlation for DC signals
3. Reshaped output of mTRFtransform to match encoding model
4. Added lag type to model summary
2.1

The following updates were made to v2.1:
Error metrics no longer based on ranked data for Spearman option
No more NaNs in multivariate metrics for Spearman option
Transformation of 3D decoders now possible
Plotting code now compatible with backward

2.0.3

Added function for CV data partitioning, added feature for equal fold generation, changed AMI metric to ADI metric for attention decoder optimization, added feature for specifying evaluation metrics.
2.0.2

New structuring of toolbox, new functions and features, new example scripts, faster and memory-efficient cross-validation, no additional MathWorks toolboxes required.
2.0.1

* Migrated parsevarargin within main functions
* Fixed bug for specifying model type
* Improved indexing and readability
* Removed coherent motion dataset
* Updated license
2.0.0

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